A Novel Sequence-Based Antigenic Distance Measure for H1N1, with Application to Vaccine Effectiveness and the Selection of Vaccine Strains

TL;DR

This paper introduces a sequence-based antigenic distance measure for H1N1 influenza that predicts vaccine effectiveness comparable to traditional ferret-based assays, aiding in better vaccine strain selection.

Contribution

The study presents a novel sequence-based method for estimating antigenic distance, improving vaccine design and effectiveness prediction for H1N1 influenza.

Findings

Sequence-based measure predicts vaccine effectiveness as well as ferret HI data.

Vaccine effectiveness is higher against H1N1 than H3N2.

H1N1 hemagglutinin evolves faster than H3N2.

Abstract

H1N1 influenza causes substantial seasonal illness and was the subtype of the 2009 influenza pandemic. Precise measures of antigenic distance between the vaccine and circulating virus strains help researchers design influenza vaccines with high vaccine effectiveness. We here introduce a sequence-based method to predict vaccine effectiveness in humans. Historical epidemiological data show that this sequence-based method is as predictive of vaccine effectiveness as hemagglutination inhibition (HI) assay data from ferret animal model studies. Interestingly, the expected vaccine effectiveness is greater against H1N1 than H3N2, suggesting a stronger immune response against H1N1 than H3N2. The evolution rate of hemagglutinin in H1N1 is also shown to be greater than that in H3N2, presumably due to greater immune selection pressure.